Dark Data in Accident Prediction: Using AdaBoost and Random Forest for Improved Accuracy

Abstract

Dark data, or unused information included into routine activities, poses significant hurdles in the era of data-driven decision-making because of its volume and complexity. The goal of this publication is to increase the accuracy of accident prediction by proposing an efficient procedure for dark data extraction and analysis. Data extraction, classifier implementation, and performance evaluation are all done in a methodical manner by using AdaBoost and Random Forest classifiers. According to the results, the Random Forest classifier outperforms the AdaBoost classifier with an accuracy of 89.50%, compared to the former's 78.4%. These results highlight the potential of dark data to yield insightful information by demonstrating how well these classifiers improve accident prediction models. In addition to emphasizing the value of dark data for decision-makers and urban planners looking to improve prediction models and access hidden information, the study offers a methodology for using it. Our research highlights the increasing significance of dark data in enhancing decision-making procedures and forecast precision as data quantities increase.